Görsel Kümelenme Eğilimi Değerlendirmesi ve R’de Uygulaması
Bölümleyici kümeleme algoritmalarında girdi olarak küme sayısı parametresi kullanılmakta ve kümelemenin başarısı büyük ölçüde analiz öncesi seçilen bu değere bağlı olmaktadır. Optimal küme sayısını bulmak için analiz sonrasında küme geçerliliği kontrolleri yapılsa da hem hesaplama zamanı maliyeti hem de kullanılan ölçütlerin veri yapılarına duyarlılığından etkilenmektedir. Bu nedenle küme sayısını analiz öncesi tahmin eden yöntemlere ihtiyaç duyulmaktadır. Görsel kümelenme eğilimi değerlendirmesi (GÖKED), küme sayısını bulmak için kullanılan öncü algoritmalardan biridir. Bu çalışmada görsel kümelenme eğilimi algoritmasının tanıtımı yapılarak R ortamında geliştirilen bir GÖKED fonksiyonu ile test edilmektedir
Anahtar Kelimeler:
Küme analizi, kümelenme eğilimi değerlendirmesi, veri görüntüleme
A Programmatic Implementation of the Visual Assessment of Cluster Tendency Algorithm in R
In cluster analysis, the partitioning algorithms require a priori estimate of number of clusters (c) as an input parameter, and thus the success of partitioning depends mostly on this parameter. In order to find an optimal c, the obtained results are checked by the various cluster validity indices at the end of each run of successive cluster analyses. Cluster validation is time consuming, and also depends on the clustering indices which may not guarantee the quality of clustering since their performances vary with complexity in data structures. In order to find an optimal number of clusters in data sets, one may benefit from the pre-analysis approaches before going to clustering. The visual assessment of clustering tendency (VAT) is a frontier algorithm which produces a grey-level image of reordered distance matrix showing existing clusters with dark blocks. This paper aims to introduce VAT algorithm and demonstrate it with a user-defined function developed in R statistical computing environment
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- ISSN: 2636-7874
- Başlangıç: 1973
- Yayıncı: Çukurova Üniversitesi